AI for All
Artificial intelligence shouldn't just be responsible — it should be equitable.
Open the AI Black Box with a New Toolbox
Principles and best practices can only get you so far. That's why EQTY has built a suite of powerful tools that ensure models are inclusive and transparent.
With AI, Integrity Means Everything
We enable anyone from developers to CEOs to shape the future of AI. Here are the basics that build a new foundation of trust in AI.
Collaboration
Empower users' voice and votes on key decisions.
Safety
Ensure many stakeholders can be part of identifying and addressing vulnerabilities.
Privacy
Always ensure users control what models know and analyze.
Transparency
Where possible, give visibility to a model's supply chain.
Accountability
Enable users to set guardrails and ensure they are enforced.
Attribution
Allow AI model rewards to be fairly shared and reinvested.
Other Al models currently have:
Models without documentation around their training data will always remain untrustworthy, as their corpus cannot be vetted for bias, misinformation, and more.
Models that do not allow nor incentivize users to contribute will always contain levels of inequality.
Models that are governed by a single entity will never be fully impartial and may be prone to security vulnerabilities in the form of single points of failure.
Models that fail to define any part of how, when, why, or by whom they were built will produce unreliable outputs as context underpins all trust.
EQTY's solutions enable:
Cryptographic primitives enable data flow to be transparent — establishing embedded accountability, verifiable lineage, and shareable metrics.
Clear data provenance encourages users to contribute to a model and invites collaboration amongst stakeholders to fine-tune the system together.
A federated governance architecture can infuse a model with programmable, democratic voting systems — rendering it more fair, sustainable, and self-sovereign.
Interactive, easy-to-understand interfaces allow technical and non-technical users alike to navigate all aspects of a model's composition.